Image segmentation generally concerns selection and/or separation of a selected part of a dataset. Such a dataset notably represents image information of an imaged object and the selected part relates to a specific part of the image. The dataset is in general a multi-dimensional dataset that assigns data values to positions in a multi-dimensional geometrical space. In particular, such datasets can be two-dimensional or three-dimensional images where the data values are pixel values, such as brightness values, grey values or color values, assigned to positions in a two-dimensional plane or a three-dimensional volume.
The invention relates to a method of segmenting a selected region from a multi-dimensional dataset, the method comprising the steps of                setting-up a shape model representing the general outline of the selected region        setting-up an adaptive mesh representing an approximate contour of the selected region        which adaptive mesh is initialized on the basis of the shape model.        
Such a method of segmenting a selected region from a three-dimensional dataset is known from the paper ‘An efficient 3D deformable model with a self-optimising mesh’ by A. J. Bulpitt and N. E. Efford in Image and Vision Computing 14(1996) pp.573–580.
The known method operates on a multi-dimensional dataset in the form of a three-dimensional image. The known method employs a triangular mesh to represent a surface of the selected region. A so-called distance transform is used to initialize the adaptive mesh and when the mesh is close to its final solution, an image grey level gradient is used to drive the deformation of the mesh.